Research projects

Learning algorithms for structured output prediction

Many real life applications correspond to problems that
cannot be described as the classification of an input into
one of a few categories or classes. These include tasks
like image labeling, machine translation, information
retrieval, speech recognition, and many others. Indeed,
the target to be predicted for such problems is typically
very high dimensional and structured.

I'm also interested in algorithms that better take into
account the loss under which the model is
evaluated. Such loss functions can have as complex a structure as the
output
itself. In Loss-sensitive
Training of Probabilistic Conditional Random Fields,
Maksim
Volkovs, Richard
Zemel and I proposed and evaluated several different
learning algorithms for doing that, focusing on the document ranking
problems.